In healthcare policy, striking a balance between equality and cost-effectiveness is an ongoing task. A common tool in this task is the Quality-Adjusted Life Year (QALY), a widely used measure in health economics for assessing the value of medical interventions. It merges both the length of life and quality of life (QoL) into a single number, enabling policymakers to compare treatments across varied health conditions. While the QALYs have been significant in informing healthcare resource utilization (HCRU) decisions, their application is becoming increasingly controversial due to the potential bias they cause against the elderly and individuals with disabilities.[1-3]
Essentially, QALY considers a year of perfect health to be worth 1, and lesser health states are counted between 0 and 1 as per the perceived QoL. However, this approach can burden those who start at a lower baseline of health, for e.g. people with disabilities, the elderly, or those receiving palliative care; because their possible health gains may happen to be smaller or less “valuable” within this framework. The QALY is under scrutiny because such valuation unfairly treats some lives as worth less than others, a drawback reinforcing the concept of “states worse than death,” (SWTD), where negative QALY scores signify reduced overall utility with prolonged life.[3] In reality, this leads to underestimating life-extending therapies for already sidelined populations. These issues have triggered debates among researchers. For instance, in 2024, the introduction of the Protecting Health Care for All Patients Act (H.R.485) stirred a controversy by restricting the use of QALYs in federal healthcare decisions.[4] While both proponents and opponents continue to argue about the issue largely on theoretical basis, the ethical impact of assigning lower value to lives lived with disability or growing age continue to be an intense challenge to the metric’s equality.[1-3]
A treatment that prolongs life is often ranked higher in QALYs for younger patients than for older ones, even though both benefit, because of the younger person’s longer life expectancy. Even if the treatment largely improves the older patient’s well-being, it may be deprioritized under strict QALY-based evaluations, efficiently assigning lower value to the lives of older individuals. This causes an ethical dilemma for researchers for whether they should account for age or remaining lifespan into decisions about whose care is “worth” more.[1, 2, 5]
Moreover, the methods used to derive the QoL weights often fail to understand and depict the perspectives of individuals living with disabilities. These weights usually depend on a general public opinion about a life with certain weaknesses, rather than on self-assessments from people actually living with those impairments. This misrepresents those individuals, lowering valuations of disabled lives, emphasizing ableist assumptions about worth and happiness.[3]
Therefore, the use of QALYs in healthcare decision-making poses a risk of causing age and disability biases, necessitating careful implementation. Health critics debate about how strongly supporting QALYs could result in unfair practices, where society inadvertently prefers the health demands of younger, able-bodied individuals. Although QALYs have been a helpful metric of health outcomes and maximum efficiency, they must be implemented with ethical consciousness, cultural sensitivity, and a motivation to select alternative or harmonizing guidelines that impartially represent the diverse human experience.
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References
Xie F, Zhou T, Humphries B, et al. Do Quality-Adjusted Life Years Discriminate Against the Elderly? An Empirical Analysis of Published Cost-Effectiveness Analyses. Value Health. 2024; 27(6):706-712.
Kocot E, Kotarba P, Dubas-Jakóbczyk K. The application of the QALY measure in the assessment of the effects of health interventions on an older population: a systematic scoping review. Arch Public Health. 2021 Nov 18;79(1):201.
Schneider P. The QALY is ableist: on the unethical implications of health states worse than dead. Qual Life Res. 2022 May;31(5):1545-1552.
Real-world evidence (RWE) studies strongly complement the conventional randomized controlled trials (RCTs), offering crucial insights into the performance of interventions in routine clinical settings. However, one of the most persistent practical concerns in RWE studies is the issue of confounding, which occurs owing to the inherent biases in real-world data (RWD). Unlike RCTs, where randomization factors in both known and unknown covariates amongst study groups, RWE studies are usually based on observational data, where treatment allocation is not arbitrary.[1] This results in confounding variables, i.e. factors that are influenced by both the treatment and the outcome, which can distort the estimated effects of interventions.[2]
The first step in addressing confounding in RWE studies is a robust study design. Researchers must be cautious while evaluating the data source, conditions for cohort selection, and timing of assessment of covariates to ensure that potential confounders are well-defined. It is essential to recognize a distinct temporal connection between exposure, confounders, and outcome. Mispositioning in these time points can result in biased associations, especially if covariates are influenced by the treatment itself or are quantified post-exposure. Careful design selection can lower this risk before applying any statistical adjustment.[2, 3]
Statistical methods play a key role in addressing confounding in RWE. Techniques like multivariable regression, inverse probability of treatment weighting (IPTW), instrumental variable analysis, and propensity score matching (PSM) are commonly applied; each method has its set of assumptions and limitations. For example, PSM can compare observed covariates between treatment groups, but they cannot justify unmeasured confounding. Instrumental variable methods, while theoretically strong, need even robust instruments, which may not be widely available in real-world datasets. These techniques seek to improve causal inference by simulating the balance achieved in RCTs. The choice of an appropriate method relies on the nature of data, the credibility of assumptions, and the research question.[2, 4, 5]
Sensitivity analyses are important tools in assessing the strength and validity of findings in the presence of residual confounding. With variable assumptions, such as the robustness of unmeasured confounding or the model specifics, researchers can evaluate how much their results might be influenced by factors not included directly. Quantitative bias analysis, E-values, and negative control outcomes are some methods that can improve the reliability of study findings. These methods do not remove confounding but help analyse the potential extent of bias.[2, 6]
Finally, transparent reporting is also essential for addressing confounding in RWE studies. Researchers should precisely define their methods for identifying, measuring, and adjusting for confounders, including the reasoning behind selected techniques and any limitations in the data. Communicating code lists, model specifications, and sensitivity analyses improves robustness and enables others to evaluate the authenticity of the findings. Established reporting guidelines, such as the STRengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement [7] for observational studies, provide a solid basis for transparency of findings. For studies considering routinely collected RWD, the REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement, which is an extension of the STROBE statement, provides additional guidance particularly to RWE complexities.[8] Implementing such frameworks facilitates clearer communication of study design and results, making RWE more reliable and actionable.[6, 8]
With the growing use of RWE in regulatory, clinical, and policy decision-making, carefully addressing confounding will be vital for ensuring reliable and actionable evidence generation.
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References
Tashkin DP, Amin AN, Kerwin EM. Comparing Randomized Controlled Trials and Real-World Studies in Chronic Obstructive Pulmonary Disease Pharmacotherapy. Int J Chron Obstruct Pulmon Dis. 2020 Jun 2;15:1225-1243.
Wang SV, Schneeweiss S. Assessing and Interpreting Real-World Evidence Studies: Introductory Points for New Reviewers. Clin Pharmacol Ther. 2022 Jan;111(1):145-149.
Laurent T, Lambrelli D, Wakabayashi R, et al. Strategies to Address Current Challenges in Real-World Evidence Generation in Japan. Drugs Real World Outcomes. 2023 Jun;10(2):167-176.
Assimon MM. Confounding in Observational Studies Evaluating the Safety and Effectiveness of Medical Treatments. Kidney360. 2021 May 14;2(7):1156-1159.
Von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Preventive medicine. 2007;45(4):247–51.
Nicholls SG, Quach P, von Elm E, et al. The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement: Methods for Arriving at Consensus and Developing Reporting Guidelines. PLoS One. 2015 May 12;10(5):e0125620.
The focus of healthcare assessments has conventionally been more on clinical and cost-efficiency of interventions. However, this tapered focus might overlook the aspects that contribute to meaningful health outcomes among individuals. The emerging concept of “Whole Health” highlights a wider, person-centred perspective of health that goes beyond physical well-being to include behavioural, emotional, social, economic, and spiritual aspects. “Whole Health” signifies a rising consensus that value in healthcare must be taken in the context of people’s lived experiences, not only through clinical parameters, but through their ability to live well.[1, 2]
This paradigm shift reflects in ISPOR’s Strategic Plan 2030,[3] that puts forth the concept of accessible, affordable, and more comprehensive healthcare that defines and delivers value.[3, 4] Inspired by the WHO’s renewed perspective on health as “a resource for everyday life, not the objective of living,”[5] ISPOR too identifies health as a comprehensive resource to advance HEOR. Consequently, assessments of health interventions must integrate a broader and more demonstrative belief of health aligning with the “Whole Health” paradigm.[2]
In reality, Whole Health value assessment integrates five basic elements: it is “people-centred, comprehensive and holistic, upstream focused, equitable and accountable, and grounded in well-being.”[2] These factors define a framework where individuals, families, communities, and healthcare systems strive together to create ‘value’. An intervention’s efficacy is not reviewed only in clinical parameters but by its ability to enhance a person’s daily life, cultivate emotional and spiritual flexibility, and minimize socioeconomic challenges. Notably, the concept of “Whole Health” promotes the incorporation of clinical, behavioural, and social care systems, all working collectively to focus on the overall health demands.[2]
The ”Whole Health” concept also encourages a more interdependent approach for generating evidence, including patient-reported outcomes (PROs), real-world data (RWD), and context-based narratives that depict different definitions of health as experienced by individuals and communities.[6] Therefore, the true health value can be assessed with components like therapeutic relationship, the patient’s personhood, and the clinician’s humanity. In Whole Health, well-being is not just the absence of disease, but the presence of a goal of contently living life and functionality, combining both hedonic (happiness-driven) and eudaimonic (meaning-driven) aspects of health.[2]
Whole Health value assessments have significant impact on HEOR in that they broaden the scope of what is evaluated, how it is measured, and whose viewpoints are prioritized.[2] By aligning with ISPOR’s strategic goals,[3] Whole Health value assessments lead more rigorous, relevant, and unbiased evaluations that represent the complexities of ever-changing modern healthcare. Whole Health also encourages stakeholders to redefine healthcare success not only by measuring disease control or economic savings, but by evaluating the quality of life lived.[1, 2, 6]
Whole Health value assessments encourage to perceive ‘value’ not only in the intervention, but also in its significance to what individuals sincerely need and care about. By doing so, they provide a more effective and more justified basis for healthcare decision-making globally.
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In health economics and quality of life research, conventional metrics often barely focus on health status or disease burden. Nonetheless, human well-being goes beyond the physical body into domains of autonomy, personal growth, and social connection. The ICEpop CAPability measure for Adults (ICECAP-A) represents a precise and innovative endeavour to summarize the wider well-being perspective by quantifying what people are able to do and be, providing an approach based on capabilities.[1, 2] Unlike the metrics for symptoms or medical interventions, the ICECAP-A instrument is developed to evaluate the individuals’ capacity to live the life they want. It moves the focus of assessments from health outcomes to life capabilities, depicting the belief that authentic quality of life goes beyond just being disease-free. This methodology facilitates researchers, policymakers, and care providers to better comprehend and cater to the wide range of factors that impact human well-being.[1, 2] Developed in 2012, the ICECAP-A instrument measures five characteristics, viz. stability (feeling of security), attachment (having support, love, and friendship), autonomy (independence), achievement (growth and progress in life), and enjoyment (experiencing joy and pleasure). This tool has four response levels and has been extensively translated. Each domain is scored through self-report, enabling individuals to contemplate how well they can perform in these aspects of life. A set of values enable its application in economic assessments through metrics including Years of Full Capability (YFC) or Sufficient Capability (YSC), supporting more impartial, capability-based decision-making. The significance of this instrument lies in its reliance on potential and opportunity, rather than only performance or achievement.[1, 2, 3] Developed with the help of qualitative interviews and thorough psychometric testing, ICECAP-A is especially important for those interventions that may or may not improve health, but substantially improve an individual’s capability to live well. For example, social care services, palliative care, mental health support, and community-based interventions usually seek to increase an individual’s sense of control, connection, and pride; outcomes often overlooked by conventional clinical measurements. The ICECAP-A tool facilitates researchers to encapsulate the value of these interventions more effusively.[1-4] Growing interest in the capability method in health economics underscores its wider assessing scope, demonstrating the impact of well-being measurements beyond outcomes that represent health-related functioning. While several regulatory bodies like NICE (UK) [5] and Zorginstituut Nederland (The Netherlands) [6] recognize the value of capability outcomes in economic assessments; these measures are still new and necessitate continuous validation and evidence review.[1] The preference-based approach of ICECAP-A facilitates economic evaluations, such as cost-utility analyses, by allotting a value to each of the five states of capability. This can further help decision-makers compare the efficacy of different services in aspects that go beyond Quality-Adjusted Life Years (QALYs) to signify what truly matters to individuals in their daily lives. [1, 2] ICECAP-A is being increasingly distinguished in public health and policy domains for providing a refined, more person-centred basis for assessing care outcomes. By focusing on human capabilities, the ICECAP-A instrument encourages researchers to approach health and social care differently. It challenges healthcare systems to give precedence to empowerment, opportunity, and contentment, rather than just symptom mitigation or disease control; thereby aligning with modern perspectives of well-being and fair, person-centred care. As the need for policies depicting the real lives and values of individuals grows, the ICECAP-A metric will be a robust tool for directing more empathetic, inclusive, and crucial decision-making.
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References
Al-Janabi H, Flynn TN, Coast J. Development of a self-report measure of capability wellbeing for adults: the ICECAP-A. Qual Life Res. 2012 Feb;21(1):167-76.
Afentou N, Kinghorn P. A Systematic Review of the Feasibility and Psychometric Properties of the ICEpop CAPability Measure for Adults and Its Use So Far in Economic Evaluation. Value Health. 2020; 23(4):515-526.
Rohrbach PJ, Dingemans AE, Groothuis-Oudshoorn CGM, et al. The ICEpop Capability Measure for Adults Instrument for Capabilities: Development of a Tariff for the Dutch General Population. Value Health. 2022; 25(1):125–132.
Rencz F, Mitev AZ, Jenei B, Brodszky V. Measurement properties of the ICECAP-A capability well-being instrument among dermatological patients. Qual Life Res. 2022 Mar;31(3):903-915.
Machine learning (ML) is revolutionizing healthcare by supporting smarter decisions and deeper insights, particularly in Health Economics and Outcomes Research (HEOR), where data-led findings impact real-world healthcare policies.[1] However, growing ML adoption is raising concerns about its reliability, transparency, and ethical use. To address these, the PALISADE Checklist provides a well-defined framework for implementing ML responsibly and reliably in HEOR.[2, 3] The framework ensures responsible adoption of ML by assessing its Purpose, Appropriateness, Limitations, Implementation, Sensitivity and Specificity, Algorithm characteristics, Data characteristics, and Explainability; thereby getting its name- the PALISADE checklist.[3]
The PALISADE Checklist is an innovative framework presented by the ISPOR Machine Learning Task Force to channel the responsible and dependable use of ML in HEOR. It takes into consideration five applications of ML methods that are crucial to HEOR; viz. 1) ML-assisted cohort selection, 2) feature selection, 3) predictive analytics, 4) causal inference, and 5) health economic evaluation, and reflection on transparency and explainability.[3] The rapid adoption of ML techniques in healthcare has necessitated a well-defined methodology to ensure transparency, consistency, and ethical foundation of these methods. PALISADE helps address ML-driven challenges by providing an extensive, standardized set of factors for researchers, analysts, and stakeholders to assess ML applications in HEOR contexts.[2, 3]
Fundamentally, the PALISADE checklist offers prompts that ML developers can use to constitute their thinking about how the suitability of ML methods can be conveyed to stakeholders and healthcare decision makers. The checklist supports comprehensibility by urging practitioners to clearly define the objectives for using ML in a given HEOR study; asking whether the ML model serves prediction, categorization, or extrapolation, and whether its objective aligns with the wider goals of the undergoing health research. This clarity of intention is crucial for accountability and also for allowing regulators, payers, and policy makers to elucidate results meaningfully as they appropriately apply them in decision-making.[3]
Another basic element of the framework is the focus on methodological relevance. ML is not a one-size-fits-all approach, and the checklist stipulates a thorough justification for choosing ML over conventional statistical methods. Researchers must weigh apparent advantages of ML pertaining to accuracy, scalability, or insight, along with the suitability of available data. This promotes the selection of ML not just as a trend, but as a carefully chosen instrument for enhancing the quality and applicability of HEOR studies.[3]
The PALISADE checklist also underscores the inherent limitations and risks in ML applications. It heavily focuses on the importance of recognizing model uncertainty, overfitting, bias, and data quality concerns that could impact the end results. The checklist urges users to openly reveal these limitations, thus reinforcing the integrity of the research and also, training stakeholders to interpret results with caution. Such transparency is crucial in HEOR, where policy decisions influence patient care and overall public health.[3]
Executional considerations also play a key role in the PALISADE checklist. It encourages researchers to contemplate how ML models will work in real-world settings, beyond controlled environments or highly curated datasets. This consists of evaluating whether the model’s extrapolations are generalizable across populations and time periods, and whether the working infrastructure is in place to incorporate the model into standard healthcare decision-making. Practical feasibility is equally important as theoretical execution for applying ML in HEOR.[3]
The framework, along with the performance metrics like accuracy or precision, clearly includes comprehensibility as an important element. Healthcare stakeholders, right from clinicians to patients to policymakers, should be capable at understanding the specific prediction made by a particular model. PALISADE supports models that provide comprehensibility outputs, while also warranting additional tools and documentation to convert complex algorithms into coherent logic. This helps lower the risk of misappropriated findings and build trust in ML tools.[3]
The final constituents of the checklist address data and algorithm features. Researchers are encouraged to document the origin, quality, and representativeness of instructions, authentication, and test datasets. Similarly, PALISADE warrants an exhaustive clarification of the algorithm’s structure, parameters, and tuning decisions. These factors are significant for reproducibility, one of the key factors of scientific accuracy, confirming that findings can be corroborated or improved upon by future researchers.[3]
Finally, the PALISADE Checklist is more than a framework as it warrants accountability in the application of ML in HEOR. By integrating ethical principles, scientific robustness, and practical insight into each phase of ML execution, it helps facilitate the powerful utilization of ML both innovatively and responsibly.
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References
Dasari M, Dasari P, Fossati S, et al. Applications of Artificial Intelligence and Machine Learning in Health Economics and Outcomes Research: A Targeted Literature Review. Value in Health. 2024; 27(12). (ISPOR Europe 2024, Barcelona, Spain).
Padula WV, Kreif N, Vanness DJ, et al. Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force. Value Health. 2022 Jul;25(7):1063-1080.
In a progressing landscape of clinical advancements, designing trials that are both scientifically robust and functionally efficient is a constant challenge. Conventional frequentist methods, while vigorous, often lack the flexibility needed to change with emerging new data in real time. This is where Bayesian methods provide a powerful advantage as they integrate prior knowledge and constantly update probabilities with continuous data. Bayesian methods present a dynamic context for trial design aligning more aptly with the multifaceted landscape of clinical research.[1, 2]
One of the most reformative characteristics of Bayesian methods is their ability to support adaptive trial designs.[3] In a Bayesian approach, emerging trial data can be used to make informed mid-trial modifications while maintaining the statistical integrity of the study. This consists of early stopping for efficacy or ineffectiveness, tweaking sample sizes, or even discontinuing ineffective treatment arms. Such adaptivity can substantially minimize the number of patients exposed to inferior treatments and expedite the regulation of successful treatment candidate, eventually saving both time and resources.[4, 5]
Another key factor that highlights the strength of Bayesian design is its ability to integrate prior information from earlier studies, expert opinion, or real-world evidence (RWE). This information is validated into probability distributions that assist in the analysis of new data, enabling more competent use of available evidence.[3] Particularly in rare diseases or early-phase studies with limited data,[6] deriving strength from prior evidence can reduce the required sample sizes, thus greatly improving decision-making and resulting in more ethical and cost-effective studies.[1-4, 6]
Bayesian methods also accelerate the development of seamless trial designs, especially in combining phases II and III. A single Bayesian design can incorporate both exploratory and confirmatory objectives rather than performing separate studies with distinct protocols and endpoints. Early trial data supports the continuation criteria, minimizing redundancy and accelerating development timelines. This seamless method can be specifically beneficial in time-sensitive therapeutic areas, including oncology or infectious diseases, where development rate can influence patient outcomes.[7]
Along with the functional efficacies, Bayesian designs excel at promoting probabilistic decision-making. Unlike binary outcomes in frequentist methods, Bayesian models offer the probability of a treatment being effective, exceeding an intended clinical threshold, or its success in a subsequent phase. These probabilities can be directly construed and applied in further decisions, portfolio management, and strategic planning. Decision-makers obtain a distinct, assessable picture of risk and benefit, which facilitates more reasonable choices under uncertainty.[3]
The adaptability of Bayesian methods also improves the researchers’ ability to study heterogeneity in treatment response, a critical aspect in today’s personalized medicine. Hierarchical Bayesian models can evaluate subgroups more intuitively, enabling researchers to identify and validate signals among populations with disparate characteristics. This allows for faster classification of potential responders or safety issues, enabling more specific and effective interventions while continuously monitoring false discovery rates.[4-7]
The acceptance of Bayesian methods has substantially grown in recent years despite some past reluctance among regulators. Regulatory agencies like the USFDA [8] and EMA [9] are now aware of the significance of Bayesian methods, especially for early-phase trials, rare diseases, and medical device authorizations.[6] These bodies underscore the importance of pre-specification, transparency, and rigorous justification in trial protocols to encourage innovative designs that strengthen Bayesian inference without compromising on regulatory integrity.[8, 9, 10]
Primarily, Bayesian tools are quite versatile. From hierarchical models to Markov Chain Monte Carlo simulations and Bayesian logistic regression, these methods are mathematically advanced and practical in managing real-world difficulties. They are completely capable of adopting uncertainty, continuously changing with new data, and providing refined insight to promote faster, improved, and more patient-centric clinical development.[1-4]
With the growing demand for smarter, more adaptive clinical trials, Bayesian methods are well-equipped to guide the next generation of trial design. Their capacity to minimize inefficiency, improve ethical conduct, and facilitate more informed decisions is beyond just a statistical advancement as it depicts an essential improvement in the development of new therapies.
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References
Muehlemann N, Zhou T, Mukherjee R, Hossain MI, Roychoudhury S, Russek-Cohen E. A Tutorial on Modern Bayesian Methods in Clinical Trials. Ther Innov Regul Sci. 2023 May;57(3):402-416.
Giovagnoli A. The Bayesian Design of Adaptive Clinical Trials. Int J Environ Res Public Health. 2021 Jan 10;18(2):530.
Ruberg SJ, Beckers F, Hemmings R, et al. Application of Bayesian approaches in drug development: starting a virtuous cycle. Nat Rev Drug Discov. 2023; 22:235–250.
Ginn GL, Campbell-Cooper C, Lockett A. The growing role of Bayesian methods in clinical trial design and analysis. Medicine. 2025.
Kidwell KM, Roychoudhury S, Wendelberger B, et al. Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles. Orphanet J Rare Dis. 2022; 17(186).
Richter J, Friede T, Rahnenführer J. Improving adaptive seamless designs through Bayesian optimization. Biom J. 2022 Jun;64(5):948-963.
Health is not just the absence of disease, but the presence of total wellbeing. For too long, conversations around public health have been driven by numbers like life expectancy, survival rates, and years lived with disease. While these metrics are essential, they often miss a critical element, the quality of those years. Longevity does not reflect better living. With the ever-evolving understanding of human health, there is a growing need for more comprehensive measures of health outcomes. This is where the concept of Wellbeing-Adjusted Life Years (WALY) becomes crucial.[1] WALY is a novel measure aimed at quantifying the effect of health interventions, diseases, or policies on overall human wellbeing, instead of focusing solely on health or longevity.[2, 3]
WALY seeks to address limitations in conventional measures, like QALY and DALY, which mainly focus on disease burden and functional impairment, often failing to capture the complete range of subjective wellbeing,[2] as it takes into consideration the overall lived experience, including happiness, emotional strength, connection, and a sense of purpose.[1, 2] With WALY, the focus shifts from merely counting years to evaluating how meaningfully and comfortably those years are lived. The incorporation of physical, mental, emotional, and social wellbeing into the health metrics offers a more humane and complete understanding of truly being healthy.[1]
WALY is usually calculated using validated subjective wellbeing scales, such as the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), which evaluates different aspects of wellbeing (such as pleasure, happiness, meaning, and purpose).[2] Supported by self-reported experiences, WALY signifies the refinements of individual health more accurately than surrogate indicators. This way, WALY aligns the assessment of emotional, psychological, and social wellbeing elements with that of physical health.[1, 2]
This shift in perspective is important as it reinvents the role of healthcare. A WALY-centred approach encourages healthcare systems to focus on overall wellbeing rather than only treating the disease and prolonging life at all costs. This approach also underscores the significance of connectedness measures, mental health services, social interventions, and community-building programs that might prolong the results in survival statistics, but profoundly improve everyday life.[1, 2]
WALY has a widespread practical impact as it facilitates comparison of the efficacy of interventions across different domains, including healthcare, social welfare, and environmental policy, by translating wellbeing outcomes into a common unit. When wellbeing becomes a key metric, investments in mental health, social infrastructure, elderly care, and proactive health gain priority and validity. This is particularly important among aging populations, where the goal is beyond simply living longer as it focuses on enabling those additional years to be filled with dignity, self-sufficiency, and joy.[1, 3] Therefore, WALY is especially valuable for decision-makers, as it informs resource allocation through a wellbeing-centred perspective.[4] By merging costs and benefits into a single, integrated framework, WALY supports the evaluation of “happiness return on investment,” highlighting interventions that maximize societal prosperity.[1]
With the specific ability to encapsulate the lived experience of individuals, WALY brings into focus areas typically understated in health economics, such as mental health, social connection, and community strength. Evidence on WALY has shown that conditions like depression and anxiety can lead to greater wellbeing losses than many physical diseases, a detail often overlooked by traditional metrics.[2-4] In this sense, WALY prioritizes psychological and emotional wellness during health assessments, promoting the development of cohesive care models catering to both mind and body. Therefore, healthcare providers are encouraged to prioritize the patients’ lived experience, their comfort, happiness, and ability to participate meaningfully in life, rather than only focusing on clinical outcomes.[1, 3]
In conclusion, WALY signifies a pivotal advancement in measuring the true impact of interventions on human health and wellbeing. By focusing on subjective wellbeing, it provides a more inclusive and human-centred approach to assessing societal progress and informing policy decisions, a reminder that true healthcare success lies not only in how long we live, but in how well we live.
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Johnson R, Jenkinson D, Stinton C, Taylor-Phillips S, Madan J, Stewart-Brown S, et al. Where’s WALY? : A proof of concept study of the ‘wellbeing adjusted life year’ using secondary analysis of cross-sectional survey data. Health Qual Life Outcomes. 2016;14(1):126.
Dizon RJR. Enhancing People’s Subjective Wellbeing: Assessing the Impact of Universal Health Coverage Through Wellbeing Adjusted Life Years. Forum for Social Economics. 2024;53(4):459-86.
Brinkmann C, Stargardt T, Brouwer WBF. From Health to Well-Being: Toward a Monetary Valuation of a Well-Being-Adjusted Life-Year. Value in Health. 2024;27(7):857-70.
The global healthcare ecosystem is undergoing a profound transformation with every advancement driven by evidence, value, and impact on overall health outcomes. Global health systems are increasingly facing pressures of rising healthcare costs, broader disparities, aging populations, and changing patient expectations.[1] A growing realization against this complex setting is that healthcare decisions must be supported with thorough, transparent research as evidence-based policies and outcomes-driven strategies increasingly gain prominence. With this evolution, the role of health economics and outcomes research (HEOR) is becoming crucial to how health interventions are developed, assessed, and approved.[2] As a result, the Professional Society for Health Economics and Outcomes Research (ISPOR), in July 2024, launched its “Strategic Plan 2030,” an elaborate proposal seeking to reimagine the global definition, measurement, and application of healthcare value.[3, 4]
The Strategic Plan 2030 essentially symbolizes universally accessible, efficient, and affordable healthcare.[3, 4] While ISPOR continues to develop HEOR excellence to enhance healthcare decision-making, the urgency to do the same has now intensified. ISPOR commits itself to six core values to guide every action – transformative philosophy, scientific reliability, inclusivity, collaboration, transparency, and ethical accuracy. These values are not merely ambitious statements, but a representation of working principles ISPOR deems necessary to deal with the increasingly complex healthcare landscape driven by technological advancements and patient outcomes. By practicing these principles, ISPOR aims to improve academic research, thereby driving real-world health improvements across populations and geographies.[5, 6]
The Strategic Plan 2030 defines two goals that outline its course. The first goal is to globally lead the definition, measurement, and use of healthcare value. In a world of competing preferences and inadequate resources, the wider definition of ‘value’ in healthcare becomes crucial. ISPOR seeks to develop a scientific basis to guide all stakeholders, including payers, providers, policymakers, and patients, toward a mutual understanding of value, which takes into consideration clinical outcomes, patient experiences, costs, and system sustainability. It is not only about producing more data but also about developing outlines for converting complex evidence into feasible, unbiased decision-making instruments.[4, 5]
ISPOR’s second goal complements the first goal of scientific aspiration in that it seeks to become the trusted HEOR agency that guides health policy change across the globe. ISPOR realizes the significance of research beyond its insights to impact the design and development of policies, allocation of their funding, and resulting care delivery. Connecting knowledge generation and policy action is difficult, necessitating technical expertise, strategic communication, building partnerships, and public trust. ISPOR foresees a future where policymakers prioritize HEOR, realizing the importance of systems proactively shaped by evidence.[5, 6]
The structure of ISPOR’s Strategic Plan 2030 is driven by three crucial global healthcare elements. The first is the increasing pressure around affordability and sustainability. Healthcare costs are rising rapidly compared to most economies, jeopardizing the feasibility of systems that fail to update and prioritize cost-effectiveness for a system-wide impact. Second, the digital revolution that is transforming the healthcare landscape, from artificial intelligence (AI) to remote monitoring to personalized medicine, necessitates new HEOR approaches for evaluating technologies to result in a continuous impact. The third element is the global change toward a whole health perspective, where well-being signifies holistic integration of physical, mental, and social health, going beyond merely the absence of disease. ISPOR’s work symbolizes this wider, more human-centric perspective of health outcomes.[3, 4]
Execution of the Strategic Plan 2030 will be fundamentally collective, signifying ISPOR’s belief that no single entity can address modern health challenges alone. ISPOR aims to encourage scientifically robust and indisputably inclusive discussion by developing platforms that connect researchers, regulators, payers, industry innovators, and patient advocacy groups. Programmes, such as the Global Health Technology Assessment (HTA) Roundtables [7] demonstrate how ISPOR aims to connect different voices to tackle common concerns, impart best practices, and lead collective development. For this, ISPOR is building an ecosystem for translating knowledge into actionable insights by means of training programs, thought leadership publications, global conferences, and multisectoral partnerships.[3, 4]
Eventually, ISPOR’s Strategic Plan 2030 is a result of a growing realization that the future of healthcare is driven not only by discoveries of new interventions or implementation of new technologies, but by making smarter, more appropriate decisions on resource utilization and value definition. The Plan is a call to action for researchers, policymakers, industry leaders, and patients to come together in this collective mission of optimizing evidence, enhancing outcomes-driven thinking, and creating a fair and sustainable healthcare future that genuinely caters to the needs of all people. Through the Strategic Plan 2030, ISPOR is outlining a course for the global health community toward a world with better decisions, achieving better health outcomes and better lives.
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References:
Hong C, Sun L, Liu G, Guan B, Li C, Luo Y. Response of Global Health Towards the Challenges Presented by Population Aging. China CDC Wkly. 2023;5(39):884-7.
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Pizzi LT, Abbott RM, Onukwugha E. Taking Health Economics and Outcomes Research Forward: Expanding the Definition of Value to Include Whole Health. Value in Health. 2025.
Healthcare interventions have traditionally been assessed primarily within the confines of the healthcare system. However, the ripple effects of these interventions extend far beyond, influencing various aspects of society, including economic productivity, educational attainment, and overall societal well-being. Recognizing and quantifying these broader impacts is crucial for a more comprehensive evaluation of healthcare interventions, leading to more informed decision-making and optimal resource allocation. Achieving this requires not only refined analytical methods but also stronger intersectoral collaborations, fostering coordinated efforts between healthcare, education, employment, social welfare, and economic sectors to ensure that data, insights, and strategies are shared across domains for maximum societal benefit.[1]
The role of health in human capital cannot be overstated. A healthy population exhibits higher productivity, increased labor force participation, and enhanced income generation. This results in higher tax revenues and greater government spending on social programs, emphasizing the critical importance of healthcare interventions in fostering economic prosperity and societal well-being.[1]
Health technology assessments (HTAs) are essential in determining the value of healthcare interventions. They assess the benefits and costs of new technologies, including pharmaceuticals, medical devices, and procedures, with the aim of informing decisions about their use and reimbursement. However, traditional HTA approaches often overlook significant benefits or harms associated with healthcare interventions that extend beyond the healthcare system itself. For instance, a treatment that reduces disability and improves productivity among patients can generate economic gains by reducing long-term disability costs, stimulating job creation, and fostering economic growth in related sectors. Yet, these broader economic impacts frequently escape the traditional HTA analysis framework. Neglecting such economic impacts in HTA evaluations may undervalue the true worth of interventions and hinder informed decision-making.[2-4]
To address these limitations, a conceptual framework has been introduced to estimate and reward the broader value of healthcare interventions. This framework employs a multifaceted approach, acknowledging direct health benefits and indirect effects on sectors like education, employment, and social services. Crucially, this framework emphasizes intersectoral collaboration, enabling healthcare decision-makers to work closely with stakeholders from education, labor, and social service sectors to capture the full range of impacts healthcare interventions generate. Such collaboration ensures that the societal value of interventions is fully understood and appropriately weighted.[5]
It integrates conventional cost-effectiveness analysis, macroeconomic methods, and a voting scheme to capture and evaluate the broader economic and societal impacts of healthcare interventions. Incorporating patient-centred value assessments within this framework can further enhance its comprehensiveness by capturing outcomes that matter most to patients, such as improvements in quality of life, functional independence, and satisfaction with care. Patient-reported outcomes, patient preferences, and lived experiences offer invaluable insights that ground this broader evaluation framework in the real-world priorities of the individuals it seeks to serve.
The distributional cost-effectiveness analysis (DCEA), which is considered as a patient-centric and equitable CEA, assesses how healthcare interventions affect different population subgroups and their equitable distribution of outcomes. While DCEA enhances HTA by considering patient-centered outcomes and equity, it stops short of capturing the broader societal impacts of health technologies. Therefore, it is recommended to augment DCEA with macroeconomic analysis to comprehensively assess the societal value of healthcare interventions.Incorporating patient-centered value assessment into DCEA would further enhance its utility by integrating both equity and personal value perspectives, ensuring that interventions deliver meaningful benefits across diverse population groups. [6]
The input-output model is a macroeconomic analysis tool traditionally used and can be effectively utilized to evaluate the extensive economic effects of healthcare interventions. This model helps to map out how different sectors of the economy interact and shows the far-reaching impact of healthcare beyond its immediate environment. By fostering intersectoral collaboration between healthcare economists, labor economists, and policymakers, input-output modeling can become even more effective in capturing cross-sector impacts, such as changes in workforce participation, educational attainment, and social service utilization. This quantitative model advocates for a holistic appreciation of healthcare’s contributions, advocating for policies that recognize and reward the full spectrum of impacts across the economy.[7]
Ali et al. (2024) introduced a structured voting scheme to guide intervention decisions by weighing health benefits against broader impacts. A voting scheme guides intervention adoption decisions, balancing health benefits against broader impacts. It categorizes interventions into four quadrants based on net health and broader impact balances, aiding decision-makers in prioritizing interventions that maximize societal benefits. Quadrant I, the ideal choice, (positive net health benefits + net broader impact) represents interventions that provide the highest value for money, actively sought after by organizations and individuals striving to maximize the impact of their allocated resources. Quadrant II (positive net health benefits + negative net broader impact), these interventions are pursued when the augmented net health benefits outweigh the negative broader impacts, justifying the investment in the intervention. Conversely, Quadrant III (negative net health benefit + negative net broader impact) interventions are deemed inadequate choices, as they engender detrimental outcomes. Options within this quadrant should be unequivocally rejected due to their deleterious effects. Lastly, Quadrant IV (negative net health benefit + positive net broader impact) includes interventions that may be pursued when the positive societal impacts outweigh the negative augmented net health benefits [5].
The voting scheme is designed to integrate interdisciplinary perspectives from multiple stakeholders, including patients, healthcare providers, policymakers, researchers, advocacy groups, payors, and community representatives, ensuring a balanced and inclusive approach to decision-making. The votes are cast to determine the adoption of interventions, facilitating a democratic and transparent process, where the diverse values and priorities of the community are reflected in the final choices.
For example, Reset-O, a US FDA-approved prescription digital therapeutic for opioid use disorder, has been shown to not only improve patient outcomes by reducing opioid use, but also to generate broader societal benefits, such as reducing health inequity and improving employment rates among treated individuals. This highlights the importance of evaluating both direct health benefits and wider economic and social outcomes when assessing healthcare interventions.[5] Similarly, large-scale vaccination programs have demonstrated positive impacts far beyond healthcare, improving school attendance rates, enhancing labor productivity, and reducing household economic vulnerability. Comprehensive mental health programs have similarly shown to improve employment retention, reduce criminal justice system involvement, and strengthen family stability. Such practical examples highlight the importance of evaluating both direct health benefits and wider economic and social outcomes when assessing healthcare interventions.[5]
In conclusion, broadening the evaluation of healthcare interventions to encompass impacts beyond the healthcare sector is essential for a comprehensive understanding of their true value. By integrating frameworks like DCEA, input-output models, and inclusive voting schemes, we can better capture the extensive economic and societal benefits these interventions offer. This holistic approach not only promotes more informed decision-making but also ensures that the contributions of healthcare interventions are fully recognized and rewarded, ultimately leading to enhanced well-being and economic growth across society.
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References:
Bleakley H. Health, human capital, and development. Annu. Rev. Econ.. 2010 Sep 4;2(1):283-310.
Goodman CS. HTA 101 Introduction to health technology assessment. 2014.
Richardson J, Schlander M. Health technology assessment (HTA) and economic evaluation: efficiency or fairness first. Journal of market access & health policy. 2019 Jan;7(1):1557981.
Angelis A, Lange A, Kanavos P. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight European countries. The European Journal of Health Economics. 2018 Jan;19:123-52.
Ali AA, Kulkarni A, Bhattacharjee S, Diaby V. Estimating and Rewarding the Value of Healthcare Interventions Beyond the Healthcare Sector: A Conceptual Framework. PharmacoEconomics. 2024 May 17:1-4.
Asaria M, Griffin S, Cookson R. Distributional cost-effectiveness analysis: a tutorial. Medical Decision Making. 2016 Jan;36(1):8-19.
Leontief W. National economic planning: methods and problems. Challenge. 1976 Jul 1;19(3):6-11.
